2012 Metrics and Analytics: Patterns of Use and Value research A report by WorldatWork and Mercer July 2012
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2012 Metrics and Analytics: Patterns of Use and Value 1 Table of Figures Table 1: Frequency of analytics used within the compensation function... 8 Figure 1: Impact on compensation decisions... 8 Table 2: Who is requesting workforce analytics... 9 Table 3: Organizations hold an adequate level of skill within the compensation function to perform the following analytics... 9 Figure 2: HR analysis performed by organizations... 9 Figure 3: Number of FTEs required for HR-related analytics... 10 Table 4: Organizations with an agreed-upon and single source definition of headcount... 10 Table 5: Raw data and tools exist to perform the following... 11 Figure 4: Raw data and/or tools exist for the following types of data... 12 Table 6: Usable and/or reliable data types... 13 Figure 5: Usable and/or reliable data types... 14 Table 7: Perceptions of data within the organization... 14 Figure 6: Future of analytics... 15 Figure 7: Total number of employees... 15 Table 8: Industry... 16 Table 9: Organization type... 16 Figure 8: Voluntary turnover... 17 Figure 9: Country of residency... 17
Introduction & Methodology 2012 Metrics and Analytics: Patterns of Use and Value 2 This report summarizes the results of a February 2012 survey of WorldatWork members to gather information about current trends in metrics and analytics. The focus of this research is to better understand what types of analytics are conducted and what technologies are used within organizations. On Feb. 15, 2012, survey invitations were sent electronically to 5104 WorldatWork members. Members selected for participation specifically noted compensation or HR generalist in their title and/or area of responsibility. The survey was open to all members domestic, Canadian and foreign meeting specific criteria. The survey closed on March 2, 2012, with 693 responses, a 14% response rate. The final dataset was cleaned, resulting in 560 responses. In order to provide the most accurate data possible, data were cleaned and analyzed using statistical software. Any duplicate records were removed. Data comparisons with any relevant, statistically significant differences are noted with this report. The demographics of the survey sample and the respondents are similar to the WorldatWork membership as a whole. The typical WorldatWork member works at the managerial level or higher in the headquarters of a large company in North America. The frequencies or response distributions listed in the report show the number of times or percentage of times a value appears in a dataset. Due to rounding, frequencies of data responses provided in this survey may not total exactly 100%.
Too Focused on Benchmarks 2012 Metrics and Analytics: Patterns of Use and Value 3 New survey shows that many organizations may not be using all of the analytical tools available in making the most effective pay decisions, continuing to rely on external/internal benchmarking techniques while not utilizing the more advanced analytical methods such as simulations and predictive modeling. Survey highlights: The 2012 Metrics and Analytics: Patterns of Use and Value Survey, conducted February 2012 by WorldatWork and Mercer, asked compensation leaders at more than 560 North American organizations how metrics and analytics are used in their decision making. Within the compensation function, organizations are more likely to use ongoing reports and benchmarking among internal and external peer groups to guide their decisions, as opposed to more sophisticated analytical techniques such as projections, simulations and predictive modeling. Furthermore, there is a higher degree of faith that these less sophisticated analytics make for better decision making. Respondents, primarily compensation practitioners, say they lack access to and confidence in data regarding education, competencies/capabilities and training investments data that are often at the heart of modern workforce analytics. In the survey of more than 500 organizations, 95% say they use analytics to externally benchmark (and 78% of them use it often), yet only 43% use it for predictive modeling (and 12% of them use it often). In fact, use of advanced tools trails off significantly as they become more sophisticated, as outlined in Exhibit 1. Whichever type of analytics is used, its use leads to better decisions, albeit at varying degrees, according to respondents. Compensation professionals may be falling behind their peers in other HR functional areas in the use of increasingly sophisticated analytics methodologies.
2012 Metrics and Analytics: Patterns of Use and Value 4 Exhibit 1 Range of analytical strength Less powerful Types of analytics used today within the compensation function perceived by participants to lead to better compensation decisions (Participants who did not use the specified analytic were excluded from this figure.) Ongoing reporting 87% 75% External benchmarks 95% 94% Internal benchmarks 89% 87% Projections 80% 71% Simulations 64% 61% More powerful Predictive modeling 43% 52% Source: WorldatWork and Mercer 2012 Metrics and Analytics: Patterns of Use and Value Survey What accounts for less usage of more powerful analytics? Inadequate skill level? Maybe. Two out of every three respondents (67%) many of whom are compensation practitioners indicate that they have an adequate level of skill to perform sophisticated analytics such as projections, simulations and predictive modeling. Limited staffing resources? Perhaps. Almost half of respondents (47%) have one to two full-time equivalent (FTE) employees responsible for HR-related analytics, which would equate to five to 10 people spending 20% of their time on analytics. Given that half of the organizations have between 1,000 and 10,000 employees, one to two FTEs sounds about right for organizations that are just starting to delve into deeper workforce analytics. Uninterested leadership? No. According to the survey, three-fourths of respondents indicate their top/c-suite executives and their HR leaders have requested workforce projections, simulations or predictive modeling. Furthermore, 74% say C-suite leadership has confidence in the accuracy and reliability of the data.
2012 Metrics and Analytics: Patterns of Use and Value 5 Limited availability and poor quality of data? Very likely according to respondents who indicate that some data are simply not available. See Exhibit 2. Moreover, 75% of respondents say they are undergoing developments to improve the consistency of their global data. And, 52% say it s unclear who has responsibility for data integrity. Frankly, we have reason to be skeptical; unavailable data may signal more of a lack of interest in the data than an ability to access it. While such data elements are often less complete or accurate than pay data, it is noteworthy that those in other parts of human resources, such as talent management and workforce planning, routinely rely on such data to determine their policies and practices. In a true total rewards environment, key variables reflecting workforce capability for example, education levels, competencies, and training and development investments should be available to track outcomes. However, there is a sharp dropoff relating to existence of key total rewards-related data and tools. This may signal a continued preoccupation of the rewards community with the behavioral or motivational side of rewards, as in assessments of the pay-performance relationship, and neglect of the asset side of the equation, that is, the effect of rewards on the ability of the organization to secure the right kinds of people. If they are not asking questions about the latter, it is no wonder they would not be insisting on acquiring these kinds of data. Exhibit 2 Raw data exists within the organization Tools exist to generate reports with this data Headcount, FTE, staffing ratios Contingent and contract employees Job function, families, roles Manager flag, supervisor ID Termination Retirement eligibility Internal promotions and transfers Performance ratings Grade/band Salaries, incentives, benefits Reporting structure Employee s educational attainment Employee s prior work experience Employee competencies Training and development investments Employee utilization of learning opportunities 0% 20% 40% 60% 80% 100%
2012 Metrics and Analytics: Patterns of Use and Value 6 Given all of the above, it is not surprising that the wish list of tasks compensation professionals would like to better explore includes at the top the following two choices (57% of respondents indicated one or both of the following): Whether our rewards strategy effectively motivates and engages our bestperforming employees Which elements of our rewards strategy (e.g., compensation, benefits, work-life, careers) effectively motivate our best-performing employees. What does it all mean? Today, the employment deal consists of much more than the competitiveness of pay or how suitably rewards are motivating employees to perform well. External analysis to gauge market competitiveness of course remains vitally important to any rewards assessment. But gauging the market competitiveness of rewards requires assessment of the value of a host of factors related to career advancement, learning and development, work environment, culture, etc. factors that cannot be fully captured in market or competitor surveys. To get at these increasingly important elements requires a deeper look within, the kind of insight that can only come from empirical analyses of the actual workforce and the business impact of specific practices. This is where high-end analytics like predictive modeling come into play. To get there, practitioners must push their thinking to look at a broader set of factors and implications for pay. They must determine what employees really value as well as how careers unfold, and how they affect an organization s workforce and drive business performance. Compensation professionals may be falling behind other HR functions in this era of big data as internal labor market data analysis and fact-based decision-making become the norm throughout human resources. This is likely not the consequence of inadequate analytical know-how indeed, historically, the compensation function has been a leader in analytical orientation and capability within human resources but more a reflection of rewards professionals focused on too narrow a set of questions concerning market competitiveness and pay-performance sensitivity, and not thinking sufficiently about the role of rewards in driving human capital development as well as current business performance.
2012 Metrics and Analytics: Patterns of Use and Value 7 As a result, professionals may want to consider: Rebuilding a culture of analytics by examining a broader set of data and utilizing more sophisticated analytical processes for critical decision making. Living up to the total rewards philosophy by tracking career velocity and movement, and accounting for important elements, such as training, education, developmental moves within an organization and lateral transfers.
2012 Metrics and Analytics: Patterns of Use and Value 8 Table 1: Frequency of analytics used within the compensation function How frequently does your organization use the following types of analytics within the compensation function? A. Ongoing reports (e.g., headcount reports, turnover reports) (n=551) B. External benchmarking (e.g., data comparisons to a standard external point of reference) (n=550) C. Internal benchmarking (e.g., data comparisons to an internal reference such as another division or line of business) (n=547) D. Projections (e.g., future forecasts based on current data) (n=535) E. Simulations (e.g., what-if scenarios) (n=543) F. Predictive modeling (e.g., statistical or regression analysis of current and historical data to make predictions about future events under multiple scenarios) (n=533) Mean Never (4) Seldom (3) Sometimes (2) Often (1) 1.51 4% 9% 21% 66% 1.27 1% 4% 17% 78% 1.47 3% 8% 23% 67% 1.85 4% 17% 41% 39% 2.21 9% 26% 40% 24% 2.67 22% 35% 31% 12% Figure 1: Impact on compensation decisions Our organization makes better compensation decisions as a result of our Participants who answered Never in Table 1 were excluded from this analysis. Strongly disagree/disagree Neutral Strongly agree/agree A. Ongoing reports (n=457) 6% 19% 75% B. External benchmarking (n=493) 1% 4% 94% C. Internal benchmarking (n=476) 3% 10% 87% D. Projections (n=441) 5% 24% 71% E. Simulations (n=408) 7% 33% 61% F. Predictive modeling (n=338) 10% 39% 52% 0% 20% 40% 60% 80% 100%
Table 2: Who is requesting workforce analytics Please rate your level of agreement with the following statements: 2012 Metrics and Analytics: Patterns of Use and Value 9 A. Our top/c-suite executives have requested workforce analytics (e.g., projections, simulations, predictive modeling) (n=468) B. Our divisional business leaders have requested workforce analytics (e.g., projections, simulations, predictive modeling) (n=461) C. Our line managers have requested workforce analytics (e.g., projections, simulations, predictive modeling) (n=452) D. Our HR leaders have requested workforce analytics (e.g., projections, simulations, predictive modeling) (n=473) Strongly disagree/disagree Neutral Strongly agree/agree 13% 11% 76% 16% 16% 68% 35% 31% 34% 13% 11% 77% Table 3: Organizations hold an adequate level of skill within the compensation function to perform the following analytics Within the compensation function, we have an adequate level of skill to conduct... A. Basic analytics such as ongoing reports and benchmarks (n=490) B. More sophisticated analytics such as projections, simulations and predictive modeling (n=483) Strongly disagree/disagree Neutral Strongly agree/agree 3% 1% 96% 17% 16% 67% Figure 2: HR analysis performed by organizations HR analysis is completed by the following in your organization (choose one): (n=495) Analysis is decentralized throughout various parts of the organization 47% Analysis is centralized in an analytics center of expertise (CoE) 39% We are decentralized but plan on creating an analytics CoE within 12 months 5% Don t know 9% 0% 20% 40% 60%
2012 Metrics and Analytics: Patterns of Use and Value 10 Figure 3: Number of FTEs required for HR-related analytics In your organization, the full-time equivalent of people responsible for HR-related analytics (e.g., would be 1 full-time equivalent [FTE] if 5 people spend 20% of their time in analytics): (n=495) 0 FTEs 13% 1-2 FTEs 47% 3-5 FTEs 19% 6-10 FTEs More than 10 FTEs 5% 4% Don t know 11% 0% 10% 20% 30% 40% 50% Table 4: Organizations with an agreed-upon and single source definition of headcount Please rate your level of agreement with the following statement: A. Our organization (including finance, IT and HR) has an agreed-upon and single source for the definition of headcount (n=459) Strongly disagree/disagree Neutral Strongly agree/agree 26% 10% 64%
Table 5: Raw data and tools exist to perform the following Please respond regarding the following types of data 2012 Metrics and Analytics: Patterns of Use and Value 11 Raw data exists within the organization Tools exist to generate reports with this data Yes Responses Yes Responses A. Headcount, FTE, staffing ratios 99% 463 93% 459 B. Contingent and contract employees 88% 417 71% 395 C. Job function, families, roles 93% 460 81% 450 D. Manager flag, supervisor ID 94% 440 90% 438 E. Terminations and termination type/reason 98% 466 92% 459 F. Retirement eligibility 88% 371 80% 365 G. Internal promotions and lateral transfers 91% 455 77% 446 H. Performance ratings 95% 459 86% 455 I. Grade/band 94% 448 89% 445 J. Salaries, incentives, benefits 98% 468 91% 458 K. Reporting structure (e.g., organizational unit) 93% 467 85% 453 L. Employee s educational attainment and background 65% 434 51% 414 M. Employee s prior work experience 50% 427 32% 408 N. Employee competencies 45% 422 39% 401 O. Training and development investments 62% 412 53% 398 P. Employee utilization of learning opportunities 59% 394 53% 387
Figure 4: Raw data and/or tools exist for the following types of data Please respond regarding the following types of data (n varies) 2012 Metrics and Analytics: Patterns of Use and Value 12 Raw data exists within the organization Tools exist to generate reports with this data Headcount, FTE, staffing ratios Contingent and contract employees Job function, families, roles Manager flag, supervisor ID Termination Retirement eligibility Internal promotions and transfers Performance ratings Grade/band Salaries, incentives, benefits Reporting structure Employee s educational attainment Employee s prior work experience Employee competencies Training and development investments Employee utilization of learning opportunities 0% 20% 40% 60% 80% 100%
Table 6: Usable and/or reliable data types Please respond regarding the following types of data 2012 Metrics and Analytics: Patterns of Use and Value 13 Generally speaking, I trust this data This data is used in decision making Yes Responses Yes Responses A. Headcount, FTE, staffing ratios 91% 463 97% 447 B. Contingent and contract employees 70% 387 76% 357 C. Job function, families, roles 84% 440 84% 424 D. Manager flag, supervisor ID 86% 428 84% 395 E. Terminations and termination type/reason 89% 460 88% 426 F. Retirement eligibility 85% 354 78% 332 G. Internal promotions and lateral transfers 77% 441 82% 407 H. Performance ratings 89% 429 90% 430 I. Grade/band 92% 440 91% 426 J. Salaries, incentives, benefits 95% 459 96% 454 K. Reporting structure (e.g., organizational unit) 83% 446 90% 428 L. Employee s educational attainment and background 49% 349 56% 332 M. Employee s prior work experience 45% 312 52% 318 N. Employee competencies 42% 310 51% 308 O. Training and development investments 54% 321 54% 313 P. Employee utilization of learning opportunities 54% 320 52% 302
Figure 5: Usable and/or reliable data types Please respond regarding the following types of data (n varies) Headcount, FTE, staffing ratios Continget/ contract employees Job function, families, roles Manager flag, supervisor ID Termination type/reason Retirement eligibility Internal promotions and transfers Performance ratings Grade/band Salaries, incentives, benefits Reporting structure Employee s education and background Employee s prior work experience Employee competencies Training and development investments Employee utilization of learning opportunities 2012 Metrics and Analytics: Patterns of Use and Value 14 Generally speaking, I trust this data This data is used in decision making 0% 20% 40% 60% 80% 100% Table 7: Perceptions of data within the organization Please rate your level of agreement with the following statements: A. Our C-suite leadership has confidence in the accuracy and reliability of our data (n=439) B. Our organization is undergoing data audits/cleanup in relation to our data (n=434) C. Our organization is undergoing developments to improve the consistency of our global/international data (n=345) D. It is clear what roles in the organization are responsible for maintaining data integrity (n=456) E. On average, the user interface of our analytics technology and tools is intuitive and easy to use (n=432) Strongly disagree/disagree Neutral Strongly agree/agree 7% 20% 74% 17% 15% 69% 9% 17% 75% 26% 22% 52% 44% 30% 26%
2012 Metrics and Analytics: Patterns of Use and Value 15 Figure 6: Future of analytics What would you like to better explore within your organization that you are unable to do today? (Choose top three.) (n=452) Whether our rewards strategy effectively motivates and engages our best-performing employees Which elements of our rewards strategy (e.g., compensation, benefits, worklife) effectively motivate our best-performing employees 57% 57% The critical drivers of employee retention in our organization 46% Whether our current sources of talent will fulfill our future business needs 40% Where we can reduce or reallocate workforce costs (e.g., headcount, benefits, compensation) without diminishing the quality of output How our rewards strategy needs to adjust to changing demographics or generational trends 34% 32% How we can effectively segment our workforce to identify critical talent segments 22% Other 1% 0% 20% 40% 60% Figure 7: Total number of employees Please choose the total number of employees your organization employs worldwide: (n=466) 20% 19% 18% 16% 14% 15% 16% 14% 12% 10% 9% 9% 8% 6% 4% 2% 4% 5% 6% 2% 0%
2012 Metrics and Analytics: Patterns of Use and Value 16 Table 8: Industry Please choose one category that best describes the industry in which your organization operates: (n=466) Industry Percent Finance & Insurance 14% All Other Manufacturing 12% Healthcare & Social Assistance 11% Utilities, Oil & Gas 7% Consulting, Professional, Scientific & Technical Services 6% Information (includes Publishing, IT Technologies, etc.) 6% Public Administration 5% Retail Trade 5% Educational Services 4% Computer and Electronic Manufacturing 4% Transportation 3% Pharmaceuticals 2% Other Services (except Public Administration) 2% Agriculture, Forestry, Fishing & Hunting 1% Wholesale Trade 1% Real Estate & Rental & Leasing 1% Arts, Entertainment & Recreation 1% Mining 1% Construction 1% Other 15% Table 9: Organization type Your organization is: (n=461) Type Percent Public sector (local, state, federal government) 20% Private sector publicly traded 38% Private sector privately held 29% Nonprofit/Not-for-profit (educational organizations, charitable organizations, etc.) 14%
2012 Metrics and Analytics: Patterns of Use and Value 17 Figure 8: Voluntary turnover What is the approximate annual voluntary turnover for employees in your organization? (n=442) 0-5% 26% 6-10% 39% 11-15% 16-20% 21-26% 27-40% 41% or more 2% 2% 4% 9% 18% 0% 10% 20% 30% 40% Figure 9: Country of residency In which country do you reside? (n=242) 80% 73% 70% 60% 50% 40% 30% 20% 10% 0% 16% 2% 2% 1% 1%